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In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadraticfunction-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation. [ABSTRACT FROM AUTHOR]

This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (TRFCMAC) model based on a dynamic-group-based hybrid evolutionary algorithm (DGHEA) for solving identification and prediction problems. The proposed T-RFCMAC model is based on the traditional CMAC model and the Takagi- Sugeno-Kang (TSK) parametric fuzzy inference system. Otherwise, the recurrent network, which imports feedback links with a receptive field cell, is embedded in the T-RFCMAC model, and the feedback units are used as memory elements. The DGHEA, which is a hybrid of the dynamic-group quantum particle swarm optimization (QPSO) and the Nelder-Mead method, is proposed for adjusting the parameters of the T-RFCMAC model. In DGHEA, an entropybased grouping technique is adopted to improve the searching capability and the convergent speed of quantum particles swarm optimization. Experimental results show that the proposed DGHEA-based T-RFCMAC model is more effective at identification and prediction than other models. [ABSTRACT FROM AUTHOR]

Background: Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences. Methods: This paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice. Results: The simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy. Conclusions: Thanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction. [ABSTRACT FROM AUTHOR]

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods. [ABSTRACT FROM AUTHOR]

The article presents a study on the adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) in solving control problems. It outlines the advantages and disadvantages of the ANFN, which motivated to the development of the MODE learning algorithm. It highlights the development of the hardware-in-the loop control technique for the ANFN controller by using MODE learning algorithm (ANFN-MODE), which has been used in planetary-train-type inverted pendulum system, and the magnetic visual simulation. The study has determined the positive result of the proposed ANFN-MODE controller.

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems. [ABSTRACT FROM PUBLISHER]

Abstract  In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi–Sugeno–Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer 2 of the QNFC model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. The simulation results have shown that (1) the QNFC model converges quickly; (2) the QNFC model has a higher correct classification rate than other models. [ABSTRACT FROM AUTHOR]

In this paper, a self-organizing quantum neural fuzzy network (QNFN) is proposed. The QNFN model is a four-layer structure. Layer 2 of the QNFN model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is also proposed. The proposed the SCA method is a fast, one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. Simulation results were conducted to show the performance and applicability of the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Thkagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer of the feedforward wavelet-based fuzzy neural networks (WFNN). An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Finally, computer simulations have demonstrated that the proposed WRFNN model requires fewer adjustable parameters and obtains a smaller rms error than other methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi-Sugeno-Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of fuzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications. [ABSTRACT FROM AUTHOR]

The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts. [ABSTRACT FROM AUTHOR]

Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. To solve this problem, this paper presents a new and effective method for image enlargement and enhancement based on adaptive inverse hyperbolic tangent (AIHT) algorithm. Conventional image enlargement and enhancement methods enlarge the image using interpolation, and subsequently enhance the image without considering image features. However, this study presents the method based on Adaptive Inverse Hyperbolic Tangent algorithm to enhance images according to image features before enlarging the image. Experimental results indicate that the proposed algorithm is capable of adaptively enhancing the image and extruding object details, thereby improving enlargements by smoothing the edge of the objects in the image. [ABSTRACT FROM AUTHOR]

The shell effects in multinucleon transfer process are investigated in the systems 136Xe + 198Pt and 136Xe + 208Pb within the dinuclear system (DNS) model. The temperature dependence of shell corrections on potential energy surface is taken into account in the DNS model and remarkable improvement for description of experimental data is noticed. The reactions 136Xe + 186W and 150Nd + 186W are also studied. It is found that due to shell effects the projectile 150Nd is more promising for producing transtarget nuclei rather than 136Xe with neutron shell closure. [ABSTRACT FROM AUTHOR]

Multinucleon transfer (MNT) reactions have been studied by either the GRAZING model or dinuclear system (DNS) model before. MNT reactions in the grazing regime have been described quite well by the GRAZING model. The DNS model is able to deal with MNT reactions, which happen in the closer overlapped regime after contact of two colliding nuclei. Since MNT reactions can happen in both areas and cannot be distinguished in view of experimental work, it is beneficial to compare these two models to clarify mechanism of MNT reactions. In this study, the mechanism of the MNT reaction has been studied by comparing the GRAZING model and DNS model for the first time. Reaction systems 136Xe+208Pb at MeV and 64Ni+238U at MeV are taken as examples in this paper. It is found that the gradients of transfer cross sections with respect to the impact parameter of the GRAZING model and DNS model are mainly concentrated on two different areas, which represents two kinds of transfer mechanisms. The theoretical framework of these two models are exclusive according to whether capture happens, which guarantees that the theoretical results calculated by these two models have no overlap and can be added up. Results indicate that the description of experimental MNT reaction cross sections can be significantly improved if calculations of the GRAZING model and DNS model are both considered. [ABSTRACT FROM AUTHOR]

Positive Q-value neutron transfer mediated sub-barrier fusion reactions are studied with an empirical coupled channels model, which takes into account neutron rearrangement related only to the dynamical matching condition with no free parameters. Fusion cross sections of collision systems 32S+90,94,96Zr are calculated and analyzed. Logarithmic residual enhancement (LRE) is proposed to evaluate the discrepancy between calculated results and experimental data. The experimental data can be described well with this model for the first time as a whole, while the LRE analysis shows that there are still theoretical systematic deviations. [ABSTRACT FROM AUTHOR]